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Study discovers neurons in the human brain that can predict what you are going to say and help you say it

By using advanced brain recording techniques, a new study demonstrates how neurons in the human brain work together to allow people to think about what words they want to say and then produce them aloud through speech.

These findings, led by researchers from Massachusetts General Hospital (MGH), provide a detailed map of how speech sounds such as consonants and vowels are represented in the brain well before they are even spoken and how they are strung together during language production.

Treatment for speech and language disorders

The work, published in the journal Nature, reveals insights into the brain’s neurons that enable language production, and could lead to improvements in the understanding and treatment of speech and language disorders.

“Although speaking usually seems easy, our brains perform many complex cognitive steps in the production of natural speech—including coming up with the words we want to say, planning the articulatory movements and producing our intended vocalizations,” says senior author Ziv Williams, MD, an associate professor in Neurosurgery at MGH and Harvard Medical School.

The researchers used a cutting-edge technology called Neuropixels, using probes to record the activities of single neurons in the prefrontal cortex. Williams and his colleagues identified cells that are involved in language production and that may underlie the ability to speak. They also found that there are separate groups of neurons in the brain dedicated to speaking and listening.

By recording individual neurons, the researchers found that certain neurons become active before this phoneme is spoken out loud. Other neurons reflected more complex aspects of word construction such as the specific assembly of phonemes into syllables.

Artificial prosthetics or brain-machine interfaces

With their technology, the scientists can predict what combination of consonants and vowels will be produced before the words are actually spoken. This capability could be leveraged to build artificial prosthetics or brain-machine interfaces capable of producing synthetic speech, which could benefit a range of patients.

The researchers hope to expand on their work by studying more complex language processes that will allow them to investigate questions related to how people choose the words that they intend to say and how the brain assembles words into sentences that convey an individual’s thoughts and feelings to others.

This work was supported by the National Institutes of Health.

Citation: Khanna, A.R., Muñoz, W., Kim, Y.J. et al. Single-neuronal elements of speech production in humans. Nature (2024). https://doi.org/10.1038/s41586-023-06982-w

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New skin patch could help you control a robotic exoskeleton

A wearable, stretchy patch could help people with mobility issues move robotic arms or legs or could assist doctors in diagnosing neurological illnesses.

The size of a BandAid, the patch sticks to your skin and picks up tiny electric signals from human muscles. In lab experiments, researchers at the Korea Advanced Institute of Science and Technology (KAIST) and the Unversity of Colorado Boulder showed that humans could use these devices to efficiently operate robotic exoskeletons (machines that mimic, and even enhance, the power of human muscles and bones). They described the new “microneedle adhesive patch” (SNAP) this month in the journal Science Advances (open access).

Microneedles

SNAP is an array of about 144 “microneedles” made of silicon coated with gold and are less than a hundredth of an inch long, making the mircroneedles hard to see with the naked eye. The microneedles only enter the top layer of your skin and aren’t long enough to reach the body’s pain sensors.

Every time you bend your arm, twist your back or even twitch a finger, currents run along your muscle fibers. Doctors typically monitor these electromyography (EMG) signals using gel electrodes that stick onto your skin. The problem: the gel dries up over time and the electrodes often slide around, resulting in poor data. 

A better EMG sensor

The researchers set out to design an EMG sensor that could function almost like a part of your body. The team’s SNAP devices are self-contained machines made of a stretchy, polymer base. They incorporate stretchable serpentine wires fabricated out of ultrathin metal.

To test out those possibilities, the researchers ran a series of experiments in their lab in which they asked people to take on an everyday task: lifting a heavy weight from the floor. In this case, the humans had a little help. They strapped on a machine that looks a bit like a knapsack and provides a robotic boost for the lower back. 

Less muscle power needed

Some of the subjects also wore SNAP devices just above their glute muscles. When the patches detected that the subjects were flexing their muscles during lifting, the devices sent a wireless signal to the robotic backpacks to begin moving. Humans wearing the patches, the team reported, used an average of 18% less muscle power while lifting than subjects who were using the robotic exoskeleton on its own.

The researchers still have a lot of work to do before their patches make it into the real world. For a start, they need to test the tools with other kinds of exoskeleton machines.

Citation: Kim, H., Lee, J., Heo, U., Jayashankar, D. K., Agno, C., Kim, Y., Kim, C. Y., Oh, Y., Byun, H., Choi, B., Jeong, H., Yeo, H., Li, Z., Park, S., Xiao, J., Kim, J., & Jeong, W. (2024). Skin preparation–free, stretchable microneedle adhesive patches for reliable electrophysiological sensing and exoskeleton robot control. Science Advances. https://www.science.org/doi/10.1126/sciadv.adk5260 (open access)

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Musk announces first neural implant in a patient

“The first human received an implant from @Neuralink yesterday and is recovering well. Initial results show promising neuron spike detection,” Elon Musk said Monday on X.

So how significant is this news?

“Whilst there are many companies working on exciting products, there are only a few other companies who have implanted their devices in humans, so Neuralink has joined a rather small group,” said Professor Anne Vanhoestenberghe, Professor of Active Implantable Medical Devices at King’s College London, writing in Expert reaction to Elon Musk reporting Neuralink has implanted wireless brain chip in a human by Science Media Center on Jan. 30.

“I expect Neuralink will want to give the participant time to recover before they start training their system with the participant,” Vanhoestenberghe said. “We know Elon Musk is very adept at generating publicity for his company, so we may expect announcements as soon as they begin testing, although true success in my mind should be evaluated in the long-term, by how stable the interface is over time, and how much it benefits the participant.”

Remote control by just thinking

“This study involves placing a small, cosmetically invisible implant in a part of the brain that plans movements,” Neuralink’s website reads. “The device is designed to interpret a person’s neural activity, so they can operate a computer or smartphone by simply intending to move—no wires or physical movement are required.

“It enables control of your phone or computer, and through them almost any device, just by thinking. Initial users will be those who have lost the use of their limbs. Imagine if Stephen Hawking could communicate faster than a speed typist or auctioneer. That is the goal.”

Telepathy

Musk nicknamed the implant as “Telepathy,” leading to some interesting speculations on X. Musk may have been inspired by an idea presented at TED 2014 by Ray Kurzweil: “We’ll have nanobots that… connect our neocortex to a synthetic neocortex in the cloud… Our thinking will be a … biological and non-biological hybrid.”

Kurzweil’s imaginative idea was explored in a 2019 paper, Human Brain/Cloud Interface (188,575 views), which focused on possible future “neuralnanorobotics” technologies. (“Full disclosure: I was co-lead author of that paper.”—Amara Angelica.) 

Meanwhile, Neuralink says “first clinical trial is open to recruitment … for people with limited or no use of both hands due to a cervical spinal cord injury or to amyotrophic lateral sclerosis, a neurological disorder that affects nerve cells.”

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Should your doctor use AI?

In an article published today in the Journal of Internal Medicine, authors at Stanford University suggest that large language models (LLMs, like ChatGPT) can be used for administrative tasks.

These tasks could include summarizing medical notes and aiding documentation; tasks related to augmenting knowledge, like answering diagnostic questions and questions about medical management; and tasks related to education.

Pitfalls

However, the authors also warn of potential pitfalls, including a lack of HIPAA adherence, inherent biases, lack of personalization, and possible ethical concerns related to text generation.

The authors also suggest checks and balances: for example, always having a human being in the loop, and using AI tools to augment work tasks, rather than replace them. In addition, the authors highlight active research areas in the field that promise to improve LLMs’ usability in health care contexts.

Citation: Jesutofunmi A. Omiye, MD, MS, Haiwen Gui, BS, Shawheen J. Rezaei, MPhil, James Zou, PhD, and Roxana Daneshjou, MD, PhD. Large Language Models in Medicine: The Potentials and Pitfalls. https://doi.org/10.7326/M23-2772

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MIT researchers invent rapid 3D printing with liquid metal

MIT researchers have developed an additive manufacturing (building one layer at a time—”3D printing” is part of the process) technique called liquid metal printing (LMP). Using liquid aluminum, it can rapidly print large-scale parts like table legs and chair frames in a matter of minutes.

The method deposits molten aluminum along a predefined path into a bed of tiny glass beads and deposits it through a nozzle at high speeds. Large-scale parts can be printed in just a few seconds (the molten aluminum cools in several minutes).

The aluminum quickly hardens into a 3D structure. The researchers say LMP is at least 10 times faster than a comparable metal additive manufacturing process, and the procedure to heat and melt the metal is more efficient than some other methods. It can also print components that are larger than those typically made with slower additive techniques, and at lower cost.

Low resolution (best for larger structures)

However, the technique sacrifices resolution (the number of dots per inch a printer can deposit) for speed and scale. But the technique would be suitable for some applications in architecture, construction, and industrial design, where components of larger structures usually don’t require extremely fine details. It could also be utilized effectively for rapid prototyping with recycled or scrap metal.

In a recent study, the researchers demonstrated the procedure by printing aluminum frames and parts for tables and chairs that were strong enough to withstand postprint machining. They showed how components made with LMP could be combined with high-resolution processes and additional materials to create functional furniture.

“But most of our built world—the things around us like tables, chairs, and buildings—doesn’t need extremely high resolution. Speed and scale, and also repeatability and energy consumption, are all important metrics,” says Skylar Tibbits, associate professor in the Department of Architecture and co-director of the Self-Assembly Lab, who is senior author of a paper introducing LMP.

Significant speedup and cooling

The team chose aluminum because it is commonly used in construction and can be recycled cheaply and efficiently. Bread loaf-sized pieces of aluminum are deposited into an electric furnace, where metal coils inside the furnace heat the metal to 700 degrees Celsius.

The aluminum is held at a high temperature in a graphite crucible, and then molten material is gravity-fed through a ceramic nozzle into a print bed along a preset path. They found that the larger the amount of aluminum they could melt, the faster the printer could go.

The process uses tiny (100-micron) glass beads to cool the metal quickly. They used LMP to rapidly produce aluminum frames with variable thicknesses that were durable enough to withstand machining processes like milling and boring.

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Ancient lake on Mars could hold traces of life

NASA Perseverance Rover’s verification of lake sediments at the base of Mars’ Jezero Crater offers new hope for finding traces of life in crater samples collected by NASA’s Perseverance rover. 

In new research published in the journal Science Advances, a team led by UCLA and The University of Oslo shows that at some point, the Jezero Crater filled with water, depositing layers of sediments on the crater floor.

Seeing below the surface of the crater for signs of life

“From orbit we can see a bunch of different deposits, but we can’t tell for sure if what we’re seeing is their original state, or if we’re seeing the conclusion of a long geological story,” said David Paige, a UCLA professor of Earth, planetary and space sciences and first author of the paper. “To tell how these things formed, we need to see below the surface.”

So as the rover drove onto the delta, Perseverance’s Radar Imager for Mars’ Subsurface Experiment (RIMFAX) instrument fired radar waves up to 20 meters below the rover, allowing scientists to see down to the base of the sediments to reveal the top surface of the buried crater floor.

Mars Perseverance Rover RIMFAX ground penetrating radar measurements of the Hawksbill Gap region of the Jezero Crater Western Delta, Mars. Hawksbill Gap (credit: Svein-Erik Hamran, Tor Berger, David Paige, University of Oslo, UCLA, California Institute of Technology Jet Propulsion Laboratory, NASA)

Perseverance’s soil and rock samples will be brought back to Earth by a future expedition and studied for evidence of past life.

Mars Sample Return: Bringing Mars Rock Samples Back to Earth

NASA and the European Space Agency are developing plans for one of the most ambitious campaigns ever attempted in space: bringing the first samples of Mars material safely back to Earth for detailed study. The diverse set of scientifically curated samples now being collected by NASA’s Mars Perseverance rover could help scientists answer the question of whether ancient life ever arose on the Red Planet. Bringing samples of Mars to Earth for future study would happen in several steps with multiple spacecraft, and in some ways, in a synchronized manner. This short animation features key moments of the Mars Sample Return campaign: from landing on Mars and securing the sample tubes to launching them off the surface and ferrying them back to Earth. Animation is contributed by NASA’s Jet Propulsion Laboratory, the European Space Agency, Goddard Space Flight Center, and Marshall Space Flight Center. Learn more: https://mars.nasa.gov/msr (Credit: NASA/ESA/JPL-Caltech/GSFC/MSFC)

Perseverance Explores the Jezero Crater Delta, Sept. 14, 2022 (credit: NASA/JPL-Caltech/AS

Citation: Paige, D. A., Hamran, E., F. Amundsen, H. E., Berger, T., Russell, P., Kakaria, R., Mellon, M. T., Eide, S., Carter, L. M., Casademont, T. M., Nunes, D. C., Shoemaker, E. S., Plettemeier, D., Dypvik, H., Holm-Alwmark, S., & N. Horgan, B. H. (2024). Ground penetrating radar observations of the contact between the western delta and the crater floor of Jezero crater, Mars. Science Advances. https://www.science.org/doi/10.1126/sciadv.adi8339 (open-access)

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Writing by hand leads to higher brain connectivity than typing on a keyboard 

What have we lost by typing on a keyboard? To find out, researchers in Norway are investigating the underlying neural networks involved in both modes of writing.

“We show that when writing by hand, brain connectivity patterns are far more elaborate than when typewriting on a keyboard,” said Prof Audrey van der Meer, a brain researcher at the Norwegian University of Science and Technology and co-author of the study published in Frontiers in Psychology.

“Such widespread brain connectivity is known to be crucial for memory formation and for encoding new information and, therefore, is beneficial for learning.”

Research design

The researchers collected EEG data from 36 university students who were repeatedly prompted to either write or type a word that appeared on a screen. When writing, they used a digital pen to write in cursive directly on a touchscreen. When typing they used a single finger to press keys on a keyboard.

High-density EEGs, which measure electrical activity in the brain using 256 small sensors sewn in a net and placed over the head, were recorded for five seconds for every prompt.

Connectivity of different brain regions increased when participants wrote by hand, but not when they typed.

Movement for memory

“We have shown that the differences in brain activity are related to the careful forming of the letters when writing by hand while making more use of the senses,” van der Meer explained. Since it is the movement of the fingers carried out when forming letters that promotes brain connectivity, writing in print is also expected to have similar benefits for learning as cursive writing.”

On the contrary, the simple movement of hitting a key with the same finger repeatedly is less stimulating for the brain. “This also explains why children who have learned to write and read on a tablet, can have difficulty differentiating between letters that are mirror images of each other, such as ‘b’ and ‘d’. They literally haven’t felt with their bodies what it feels like to produce those letters,” van der Meer said.

Citation: H., A. L. (2024). Handwriting but not typewriting leads to widespread brain connectivity: A high-density EEG study with implications for the classroom. Frontiers in Psychology, 14, 1219945. https://doi.org/10.3389/fpsyg.2023.1219945 (open access)

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Scientists Hack DNA to Make Next-Gen Nanostructures

Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Columbia University, and Stony Brook University have developed a radical new method for producing a wide variety of designed metallic and semiconductor 3D nanostructures. The method uses a “hacked” form of DNA that instructs molecules to organize themselves into targeted 3D patterns.

The new method could produce robust nanostructures from multiple material classes. The study was recently published in Science Advances.

“We have been using DNA to program nanoscale materials for more than a decade,” said corresponding author Oleg Gang, a professor of chemical engineering and of applied physics and materials science at Columbia Engineering and leader of the Soft and Bio Nanomaterials Group at the Center for Functional Nanomaterials (CFN), a DOE Office of Science user facility at Brookhaven Lab.

“Now, by building on previous achievements, we have developed a method for converting these DNA-based structures into many types of functional inorganic 3D nano-architectures, and this opens tremendous opportunities for 3D nanoscale manufacturing.”

Next level of self-assembly: microelectronics and semiconductor devices

CFN is a leader in researching self-assembly, the process by which molecules spontaneously organize themselves. In particular, scientists at CFN are experts at DNA-directed assembly. Researchers program strands of DNA to “direct” the self-assembly process towards molecular arrangements that give rise to beneficial properties, such as electrical conductivity, photosensitivity, and magnetism. Then, those structures can be scaled up to functional materials.

To date, CFN has used DNA-directed assembly to produce switchable thin films, 3D nanosuperconductors, and more.

3D metallic nanostructures

“We have demonstrated various types of structures we can organize using DNA-directed assembly. But, to take this research to the next level, we can’t only rely on DNA,” Gang said. “We needed to expand upon our method to make more robust structures with more specific functionality for advanced technologies like microelectronics and semiconductor devices.”

Recently, Gang and colleagues were able to grow silica, an oxidized form of silicon, onto a DNA lattice. The addition of silica created a much more robust structure, but the procedure was not widely applicable to different materials. The team still needed further research to develop a method that could produce metallic and semiconductor materials in an efficient way.

So to build out a more universal method for producing 3D nanostructures, researchers in CFN’s Soft and Bio Nanomaterials Group collaborated with the Center’s Electronic Nanomaterials Group.

Scientists in this group pioneered a novel material synthesis technique called vapor-phase infiltration. This technique bonds a precursor chemical, in vapor form, to a nanoscale lattice, penetrating beyond the surface and deep into the material’s structure. Conducting this technique on the silica structures Gang’s team had previously built, using precursors with metallic elements, enabled the researchers to produce 3D metallic structures.

The team also experimented with liquid-phase infiltration, a technique that forms chemical bonds on a material’s surface, except with a liquid precursor. In this case, the team bonded different metal salts to silica, forming a variety of metallic structures. For example, they were able to combine platinum, aluminum, and zinc on top of one nanostructure.

The team was able to produce 3D nanostructures containing different combinations of zinc, aluminum, copper, molybdenum, tungsten, indium, tin, and platinum. This is the first demonstration of its kind for creating highly structured 3D nanomaterials.

There are several properties needed to make useful materials for technologies like semiconductor devices. For this study, the researchers imparted electrical conductivity and photoactivity on the 3D nanostructures.

Making world-leading research accessible; a liquid-handling robot

CFN will now work to apply the method to more complex research and offer it to visiting scientists. As a user facility, CFN makes its capabilities and expertise available to “users” across the country and the world.

The ecosystem of CFN’s expertise and facilities that benefited this research is also a benefit to users, and CFN is constantly expanding its offerings and making them more accessible. For example, scientists are looking to implement the new research method into one of the Center’s newest tools, a liquid-handling robot.

CFN also studies the mechanical properties of nanomaterials, and the materials like the ones developed in this work hold great potential for enhancing mechanical performance, as was recently shown by the group in another study, the researchers say.

“Overall, CFN’s new method for creating designed, robust, and functionally tunable 3D nanostructures has set the stage for breakthroughs in advanced manufacturing at small scales. Their work could enable diverse emerging technologies, and it will provide new opportunities for science initiatives and users at Brookhaven Lab.”

This study was supported by the DOE Office of Science.

Citation: Michelson, A., Subramanian, A., Kisslinger, K., Tiwale, N., Xiang, S., Shen, E., Kahn, J. S., Nykypanchuk, D., Yan, H., Nam, Y., & Gang, O. (2024). Three-dimensional nanoscale metal, metal oxide, and semiconductor frameworks through DNA-programmable assembly and templating. Science Advances. https://www.science.org/doi/10.1126/sciadv.adl0604 (open-access)

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Bad-actor AI activity will escalate by mid-2024, amplifying disinformation in election season: GWU researchers

A new study led by researchers at the George Washington University (GWU) predicts that daily, bad-actor AI activity is going to escalate by mid-2024, increasing the threat that it could affect election results in 50 countries. The research, published today in the journal PNAS Nexus, is the first quantitative scientific analysis that predicts how bad actors will misuse AI globally.

Among their findings:

  • Bad actors need only basic Generative Pre-trained Transformer (GPT) AI systems to manipulate and bias information on platforms, rather than more advanced systems such as GPT 3 and 4, which tend to have more guardrails to mitigate bad activity.
  • A road network across 23 social media platforms will allow bad-actor communities direct links to billions of users worldwide without users’ knowledge.
  • Bad-actor activity driven by AI will become a daily occurrence by the summer of 2024*
  • Social media companies should deploy tactics to contain the disinformation, as opposed to removing every piece of content. According to the researchers, this looks like removing the bigger pockets of coordinated activity while putting up with the smaller, isolated actors.

* To determine this, the researchers used proxy data from two historical, technologically similar incidents that involved the manipulation of online electronic information systems. The first set of data came from automated algorithm attacks on U.S. financial markets in 2008, and the second came from Chinese cyber attacks on U.S. infrastructure in 2013. By analyzing these data sets, the researchers were able to extrapolate the frequency of attacks in these chains of events and examine this information in the context of the current technological progress of AI.

The open-access paper, “Controlling bad-actor-AI activity at scale across online battlefields,” was published in the journal PNAS Nexus. The research was funded by the U.S. Air Force Office for Scientific Research and The Templeton Foundation. 

Citation: Cross online battlefields. PNAS Nexus, 3(1). https://doi.org/10.1093/pnasnexus/pgae004 (open-access)

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IntelliGenes accessible AI software helps predict diseases

To help predict diseases, researchers at Rutgers Health have developed IntelliGenes software, which combines artificial intelligence (AI) and machine-learning approaches.

A study published in Bioinformatics explains how IntelliGenes can be used by a wide range of users to analyze multigenomic and clinical data. It’s accessible by anyone, says Zeeshan Ahmed, lead author of the study and a faculty member at Rutgers Institute for Health, Health Care Policy and Aging Research (IFH).

Personalized patient predictions

Previously, there were no AI or machine-learning tools available to investigate and interpret the complete human genome, especially for non-experts. So Ahmed and members of his Rutgers lab developed IntelliGenes software. It combines conventional statistical methods with cutting-edge machine-learning algorithms to produce personalized patient predictions and a visual representation of the biomarkers significant to disease prediction.

In another study, published in Scientific Reports, the researchers applied IntelliGenes to discover novel biomarkers and predict cardiovascular disease with high accuracy.

“There is huge potential in the convergence of datasets and the staggering developments in artificial intelligence and machine learning,” said Ahmed, who is also an assistant professor of medicine at Robert Wood Johnson Medical School.

Early detection of common and rare diseases

“IntelliGenes can support personalized early detection of common and rare diseases in individuals, as well as open avenues for broader research ultimately leading to new interventions and treatments,” said Ahmed.

The researchers tested the software using Amarel, a high-performance computing cluster managed by the Rutgers Office of Advanced Research Computing.

Citation: DeGroat, W., Mendhe, D., Bhusari, A., Abdelhalim, H., Zeeshan, S., & Ahmed, Z. (2023). IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics, 39(12). https://doi.org/10.1093/bioinformatics/btad755

Citation: DeGroat, W., Abdelhalim, H., Patel, K. et al. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci Rep 14, 1 (2024). https://doi.org/10.1038/s41598-023-50600-8

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